fashion_mnist.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""FashionMNIST Data Set"""


import struct

import numpy as np

import datasets
from datasets.tasks import ImageClassification


_CITATION = """\
@article{DBLP:journals/corr/abs-1708-07747,
  author    = {Han Xiao and
               Kashif Rasul and
               Roland Vollgraf},
  title     = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
               Algorithms},
  journal   = {CoRR},
  volume    = {abs/1708.07747},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.07747},
  archivePrefix = {arXiv},
  eprint    = {1708.07747},
  timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""

_DESCRIPTION = """\
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of
60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image,
associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in
replacement for the original MNIST dataset for benchmarking machine learning algorithms.
It shares the same image size and structure of training and testing splits.
"""

_HOMEPAGE = "https://github.com/zalandoresearch/fashion-mnist"
_LICENSE = "https://raw.githubusercontent.com/zalandoresearch/fashion-mnist/master/LICENSE"

_URL = "https://github.com/zalandoresearch/fashion-mnist/raw/master/data/fashion/"
_URLS = {
    "train_images": "train-images-idx3-ubyte.gz",
    "train_labels": "train-labels-idx1-ubyte.gz",
    "test_images": "t10k-images-idx3-ubyte.gz",
    "test_labels": "t10k-labels-idx1-ubyte.gz",
}

_NAMES = [
    "T - shirt / top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]


class FashionMnist(datasets.GeneratorBasedBuilder):
    """FashionMNIST Data Set"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="fashion_mnist",
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                }
            ),
            supervised_keys=("image", "label"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[ImageClassification(image_column="image", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": [downloaded_files["train_images"], downloaded_files["train_labels"]],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": [downloaded_files["test_images"], downloaded_files["test_labels"]],
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """This function returns the examples in the raw form."""
        # Images
        with open(filepath[0], "rb") as f:
            # First 16 bytes contain some metadata
            _ = f.read(4)
            size = struct.unpack(">I", f.read(4))[0]
            _ = f.read(8)
            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)

        # Labels
        with open(filepath[1], "rb") as f:
            # First 8 bytes contain some metadata
            _ = f.read(8)
            labels = np.frombuffer(f.read(), dtype=np.uint8)

        for idx in range(size):
            yield idx, {"image": images[idx], "label": int(labels[idx])}